Abstract

The RBF-ARX model has been used intensively in modeling and control of nonlinear systems, in which the coefficients of the NARX model are approximated with RBF networks. In this paper, motivated by the fact that the state feedback control policy with variable-gain feedback can support more freedom for the design of RMPCs, we propose an RBF-ARX model-based variable-gain feedback RMPC synthesis method. First, a polytopic state space model construction method is designed, in which the variation rate information of the model parameters is also utilized to improve accuracy of the system model prediction. And then, a robust variable-gain feedback predictive control algorithm is designed to enlarge design freedom and improve control performance. Finally, the verification of the feasibility and effectiveness of our RMPC is conducted on a CSTR process.

Highlights

  • Robust model predictive control (RMPC) attracts lots of attention due to its advantages for handling the system uncertainties and constraints [1], [2], and have been widely used in studying the polytopic dynamic system because polytopic model can conveniently include the dynamic characteristics of complex systems [3]

  • The RMPC synthesis method was first proposed by Kothare et al [4]

  • We present a data-driven input-output model based RMPC synthesis method

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Summary

INTRODUCTION

Robust model predictive control (RMPC) attracts lots of attention due to its advantages for handling the system uncertainties and constraints [1], [2], and have been widely used in studying the polytopic dynamic system because polytopic model can conveniently include the dynamic characteristics of complex systems [3]. F. Zhou et al.: RBF-ARX Model-Based Variable-Gain Feedback RMPC Algorithm bounded disturbance as well as polytopic uncertainties. To improve the future state space model prediction [37], the variation rate of RBF-ARX model parameters is considered By considering both the future system model and the robust controller with a variable-gain feedback, we can further improve the control performance of the RMPC algorithm. Based on the above method, one can see that a group of the ‘gradually increases’ polytopic sets can be constructed to wrap the rest state matrix set Ak+j|k , Bk+j|k |j ≥ 2 in (8) This method significantly increases the online calculation burden of the algorithm, especially when the RBF-ARX model order is high.

SIMULATION STUDY
Findings
CONCLUSIONS

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